Random Key Optimizer for the Flexible Job Shop Scheduling Problem with Learning Effect: Modeling and Computational Results

This paper investigates the application of the Random Key Optimizer (RKO) framework to the Flexible Job Shop Scheduling Problem with learning effects. By refining the decoding procedure and employing appropriate parameterization strategies, it was possible to obtain higher-quality solutions than those produced by the commercial solver Gurobi v12 for small-sized instances. The obtained results indicate the potential of the framework for future applications to more complex instances and more realistic learning models.